84,842 research outputs found
Learning Algorithms of Form Structure for Bayesian Networks
ISBN : 978-1-4244-7993-1International audienceIn this paper, a new method is presented for the recognition of online forms filled manually by a digital-type clip. This writing process is not very restrictive but it is only sending electronic ink without the pre-printed form, which will require to undertake field recognition without context. To identify the form model of filled fields, we propose a method based on Bayesian networks. The networks use the conditional probabilities between fields in order to infer the real structure. We associate multiple Bayesian networks for different structures levels (i.e. sub-structures) and test different algorithms for form structure learning. The experiments were conducted on the basis of 3200 forms provided by the Actimage compagny, specialist in interactive writing processes. The first results show a recognition rate reaching more than 97%
Learning All Credible Bayesian Network Structures for Model Averaging
A Bayesian network is a widely used probabilistic graphical model with
applications in knowledge discovery and prediction. Learning a Bayesian network
(BN) from data can be cast as an optimization problem using the well-known
score-and-search approach. However, selecting a single model (i.e., the best
scoring BN) can be misleading or may not achieve the best possible accuracy. An
alternative to committing to a single model is to perform some form of Bayesian
or frequentist model averaging, where the space of possible BNs is sampled or
enumerated in some fashion. Unfortunately, existing approaches for model
averaging either severely restrict the structure of the Bayesian network or
have only been shown to scale to networks with fewer than 30 random variables.
In this paper, we propose a novel approach to model averaging inspired by
performance guarantees in approximation algorithms. Our approach has two
primary advantages. First, our approach only considers credible models in that
they are optimal or near-optimal in score. Second, our approach is more
efficient and scales to significantly larger Bayesian networks than existing
approaches.Comment: under review by JMLR. arXiv admin note: substantial text overlap with
arXiv:1811.0503
Bayesian Discovery of Multiple Bayesian Networks via Transfer Learning
Bayesian network structure learning algorithms with limited data are being
used in domains such as systems biology and neuroscience to gain insight into
the underlying processes that produce observed data. Learning reliable networks
from limited data is difficult, therefore transfer learning can improve the
robustness of learned networks by leveraging data from related tasks. Existing
transfer learning algorithms for Bayesian network structure learning give a
single maximum a posteriori estimate of network models. Yet, many other models
may be equally likely, and so a more informative result is provided by Bayesian
structure discovery. Bayesian structure discovery algorithms estimate posterior
probabilities of structural features, such as edges. We present transfer
learning for Bayesian structure discovery which allows us to explore the shared
and unique structural features among related tasks. Efficient computation
requires that our transfer learning objective factors into local calculations,
which we prove is given by a broad class of transfer biases. Theoretically, we
show the efficiency of our approach. Empirically, we show that compared to
single task learning, transfer learning is better able to positively identify
true edges. We apply the method to whole-brain neuroimaging data.Comment: 10 page
Who Learns Better Bayesian Network Structures: Accuracy and Speed of Structure Learning Algorithms
Three classes of algorithms to learn the structure of Bayesian networks from
data are common in the literature: constraint-based algorithms, which use
conditional independence tests to learn the dependence structure of the data;
score-based algorithms, which use goodness-of-fit scores as objective functions
to maximise; and hybrid algorithms that combine both approaches.
Constraint-based and score-based algorithms have been shown to learn the same
structures when conditional independence and goodness of fit are both assessed
using entropy and the topological ordering of the network is known (Cowell,
2001).
In this paper, we investigate how these three classes of algorithms perform
outside the assumptions above in terms of speed and accuracy of network
reconstruction for both discrete and Gaussian Bayesian networks. We approach
this question by recognising that structure learning is defined by the
combination of a statistical criterion and an algorithm that determines how the
criterion is applied to the data. Removing the confounding effect of different
choices for the statistical criterion, we find using both simulated and
real-world complex data that constraint-based algorithms are often less
accurate than score-based algorithms, but are seldom faster (even at large
sample sizes); and that hybrid algorithms are neither faster nor more accurate
than constraint-based algorithms. This suggests that commonly held beliefs on
structure learning in the literature are strongly influenced by the choice of
particular statistical criteria rather than just by the properties of the
algorithms themselves.Comment: 27 pages, 8 figure
Learning Bayesian Networks with the bnlearn R Package
bnlearn is an R package which includes several algorithms for learning the
structure of Bayesian networks with either discrete or continuous variables.
Both constraint-based and score-based algorithms are implemented, and can use
the functionality provided by the snow package to improve their performance via
parallel computing. Several network scores and conditional independence
algorithms are available for both the learning algorithms and independent use.
Advanced plotting options are provided by the Rgraphviz package.Comment: 22 pages, 4 picture
Bayesian Network Structure Learning with Permutation Tests
In literature there are several studies on the performance of Bayesian
network structure learning algorithms. The focus of these studies is almost
always the heuristics the learning algorithms are based on, i.e. the
maximisation algorithms (in score-based algorithms) or the techniques for
learning the dependencies of each variable (in constraint-based algorithms). In
this paper we investigate how the use of permutation tests instead of
parametric ones affects the performance of Bayesian network structure learning
from discrete data. Shrinkage tests are also covered to provide a broad
overview of the techniques developed in current literature.Comment: 13 pages, 4 figures. Presented at the Conference 'Statistics for
Complex Problems', Padova, June 15, 201
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